Category induction from distributional cues in an artificial language.

Abstract

The ability to identify the grammatical category of a word (e.g., noun, verb, adjective) is a fundamental aspect of competence in a natural language. Children show evidence of categorization by as early as 18 months, and in some cases younger. However, the mechanisms that underlie this ability are not well understood. The lexical co-occurrence patterns of words in sentences could provide information about word categories--for example, words that follow the in English often belong to the same category. As a step in understanding the role distributional mechanisms might play in language learning, the present study investigated the ability of adults to categorize words on the basis of distributional information. Forty participants listened for approximately 6 min to sentences in an artificial language and were told that they would later be tested on their memory for what they had heard. Participants were next tested on an additional set of sentences and asked to report which sentences they recognized from the first 6 min. The results suggested that learners performed a distributional analysis on the initial set of sentences and recognized sentences on the basis of their memory of sequences of categories of words. Thus, mechanisms that would be useful in natural language learning were shown to be active in adults in an artificial language learning task.